Image deformation using multiple image regions

ABSTRACT

Disclosed are systems for and methods of registering (i.e., aligning) a deformable image with a reference image subject to a plurality of regions within the deformable and reference images. Different members of the plurality of regions may be used in different phases of a deformation algorithm and the identity of these regions may change between different iterations of the deformation algorithm. In some embodiments, most of an image is used for calculation of the internal force of the demons algorithm while a smaller subset of the image is used for calculating the opposing external force.

BACKGROUND

1. Field of the Invention

The present invention relates generally to the field of imageregistration and more specifically to the field of image registrationconfigured for medical applications.

2. Related Art

Deformable image registration is a technique for modifying a deformableimage in an elastic way to match similar features in a reference image.The technique, in general, involves determining a transform necessary toregister (e.g., to align) two images by matching structures of interestin the deformable image and the reference image and to deform thedeformable image to achieve alignment of these structures. Thedeformable and reference images may be images of a patient at differenttimes. The deformable image may be an image of a healthy person and thereference image may be an image of a patient, or vice versa.

For example, the deformable image may include an image of a patient inwhich a physician has carefully delineated a treatment volume, and thereference image may include an image of the same patient recorded at alater time during treatment. A treatment volume is a spatial volumewithin a patient to be treated using radiation such as X-rays orparticle beams. By deforming the carefully delineated image to alignwith an image recorded during treatment, movement in the treatmentvolume can be readily identified. The unaltered reference image, havinga treatment volume identified through the image registration process,can then be used to target the new position of the treatment volumeduring treatment.

The technique has various medical applications. One medical applicationincludes identification of movement or change in organs in medicalimages acquired at different points in time, for example, changes inpatient anatomy since an image was taken at the outset of a treatmentplan. Movement or changes in organs can occur due to the spread ofcancer, bladder fullness, swelling, breathing, etc.

Another medical application of the deformable image registrationtechnique is segmentation of images using a pre-segmented atlas againstwhich the images are registered. After registration is complete, anidentity and/or scope of an anatomical structure within the referenceimage can be established automatically using previous identification ofa. corresponding structure within the deformable image. Both medicalapplications allow for the identification of, for example, changes in anorgan during the course of treatment such that the treatment may bealtered or discontinued.

Typically, the deformable image registration technique involvesdetermination of a mapping between the two images. This mapping may bebi-directional, e.g., it may be used to map from one image to the otherin either direction. As such, the labeling of the two images as the“deformable” image and the “reference” image is typically a conventionused for convenience.

The mapping between the two images can be represented by a deformationfield (e.g., transformation). For example, the deformation field may berepresented by a set of transform vectors representative of thedislocation of pixels between images. Determination of the deformationfield includes use of a deformation algorithm that is iterativelyapplied in two phases. A first phase includes execution of an imagebased similarity force that alters the deformation field such that theimage intensities within the two images match more closely on a pixel bypixel basis. In another phase, a flexibility model is applied to thedeformation field. This flexibility model may include determining aninternal force field, represented by a set of vectors, that are opposedto the image similarity force.

Various deformation algorithms exist in the prior art, for example, thedemons algorithm. The flexibility model in the demons algorithmcomprises Gaussian smoothing of the field of displacement vectors. A“similarity” force, opposed to the effect of smoothing displacementvectors, is then applied by using a normalized optical flow. Oneadvantage of the demons algorithm is the speed at which the steps of thealgorithm are performed (e.g., on the order of minutes).

A disadvantage of existing deformation algorithms is that all regions ofan image are treated equally in each phase. As such, in the registrationof an image including both a first organ and a second organ ofrelatively great interest, those parts of the image including each organare equally weighted. This may be a disadvantage if the primary purposeof the registration process is to match the second organ.

There is, therefore, a need for improved systems and methods ofregistering medical images.

SUMMARY

Various embodiments include systems for and methods of registering(i.e., aligning) a deformable image with a reference image subject to aplurality of different regions within the reference and/or deformableimages. These different regions may be separate or overlapping. Forexample, a first region may include most of an image while a secondregion may include a subset of the first region. The first region may beemphasized during an initial course localization and the second regionmay be more heavily weighted in a subsequent more accurate refinementaccording to a registration algorithm. This weighting may changeprogressively over time. The identity of the second region may changeduring the registration process.

In some embodiments, different parts of a registration algorithm areexecuted using different regions as input. For example, as discussedfurther herein, the demons algorithm includes phases in which externaland internal forces are alternatively applied. These forces may becalculated using different regions within the images as input, e.g., theinternal force may be calculated using image date from the second regionwhile the external force is calculated using image data from the firstregion. When a registration algorithm is executed using a particularregion as input, the results of this execution may be applied to justthe particular region, to another region or to the entire image. Use ofa region, as discussed herein, includes using image data from the regionas input to all or part of a registration algorithm. Image data mayinclude pixel intensity, flexibility, forces, quality of match, a prioriproperties, accumulators, displacement vectors, other features of animage, and/or the like.

Various embodiments include extensions to the demons algorithm of theprior art, although the systems and methods described herein may beapplied to other image registration algorithms. The demons algorithmcomprises two phases of determining an image transformation. In onephase, the image transformation is determined using a path of leastresistance under the influence of an external force. This force isconsidered external because a transform vector associated with eachpixel is determined by considering intensities of pixels in thereference image so as to minimize the differences in intensities betweenthe deformable image and the reference image. In another phase, thedemons algorithm reconnects the pixels using a smoothing model appliedto the image transformation. This internal force tends to keep thepixels together to prevent the entire deformable image from beingdissolved by the image transformation. The internal force tends to applya force to the pixels opposing the external force of the first phase. Inorder to determine the image transformation that will deform thedeformable image to match the reference image, these two phases of thedemons algorithm are typically repeated in an iterative process. Variousembodiments further include a repository for storing images and/or otherdata associated with a deformation engine, a computing system forstoring part or all of the data associated with the deformationalgorithm, and an image generation apparatus for printing or displaying,for example, a deformed image.

Various embodiments of the invention include an image registrationsystem comprising a deformation engine including first logic configuredto compute a flexibility model using a first region within a medicaldeformable image and to apply the computed flexibility model to themedical deformable image to generate a set of displacement vectors, theset of displacement vectors being configured to apply a first force tothe medical deformable image, second logic configured to compute asmoothing function using the first region within the medical deformableimage and to apply the smoothing function to the set of displacementvectors to create a smoothed set of displacement vectors, third logicconfigured to compute a transform using a second region of the medicaldeformable image, the transform being configured to apply a second forceto the medical deformable image, the second force opposing the firstforce, and fourth logic configured to apply the smoothed set ofdisplacement vectors and the transform to the medical deformable imagein one of a plurality of iterations to deform the medical deformableimage to match a reference image.

Various embodiments of the invention include an image registrationsystem comprising a deformation engine including first logic configuredto compute a flexibility model using a medical deformable image, and toapply the computed flexibility model to the medical deformable image togenerate sets of displacement vectors, the sets of displacement vectorsbeing configured to apply a first force to the medical deformable image,second logic configured to compute smoothing functions using a firstregion of the medical deformable image and to apply the computedsmoothing function to the sets of displacement vectors to create asmoothed set of displacement vectors, third logic configured to computetransforms using a first region and a second region of the medicaldeformable image, the transform being configured to apply a second forceto the medical deformable image, the second force opposing the firstforce, and fourth logic configured to apply the smoothed sets ofdisplacement vectors and the transforms to the medical deformable imagein an iterative process to deform the medical deformable image to matcha reference image, at least one iteration of the iterative processincluding use of the first region to compute one of the smoothingfunctions and use of the second region to compute one of the transforms.

Various embodiments of the invention include a method comprisingcomputing a set of displacement vectors according to a flexibility modelusing a first region of a medical deformable image, the set ofdisplacement vectors being configured to apply a first force to themedical deformable image, applying a smoothing function to the set ofdisplacement vectors to create a smoothed set of displacement vectors,computing a transform using a second region of the medical deformableimage, the transform being configured to apply a second force to themedical deformable image, the second force opposing the first force andbeing different from the first region, and applying the transform andthe smoothed set of displacement vectors to the medical deformable imagein an iteration of a deformation algorithm configured to match themedical deformable image to a reference image.

Various embodiments of the invention include an image deformation enginecomprising first logic configured to compute a plurality of weightsassociated with a first region within a medical deformable image, and touse the plurality of weights to determine a first set of force vectors,the first set of force vectors being configured to apply a first forceto an image transformation between the medical deformable image and areference image, second logic configured to compute a flexibility modelusing a second region within the medical deformable image and to applythe flexibility model to generate a second set of force vectors, thesecond set of force vector being configured to apply a second force tothe image transformation, the second force opposing the first force, andthird logic configured to apply the first set of force vectors and thesecond sets of force vectors to the image transformation in one of aplurality of iterations to match the medical deformable image and thereference image.

Various embodiments of the invention include an image deformation enginecomprising first logic configured to compute a plurality of weightsassociated with a first region within a medical deformable image and touse the plurality of weights to determine a first set of force vectors,the first set of force vectors being configured to apply a force to animage transformation between the medical deformable image and areference image, second logic configured to compute a flexibility modelusing a second region within the medical deformable image and to applythe flexibility model to the image transformation, and third logicconfigured to use the first logic and the second logic in one of aplurality of iterations to generate the image transformation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates various embodiments of an image registration system;

FIG. 2 illustrates various embodiments of a deformation engine;

FIG. 3 illustrates various embodiments of a force computation engineconfigured to use more than one region to deform an image;

FIG. 4 illustrates various embodiment of a repository; and

FIG. 5 illustrates methods of deforming an image, according to variousembodiments.

DETAILED DESCRIPTION

Various embodiments of the invention include systems for and methods ofregistering (i.e., aligning) a deformable image with a reference imageusing a plurality of different regions within the reference image. Theregistration is accomplished using a registration system and includesdetermining an image transformation configured to modify the deformableimage (e.g., an image of a healthy individual used as a template) tomatch similar features in the reference image (e.g., an image of apatient). The registration system includes a deformation engineconfigured for performing a deformation algorithm. In some embodiments,this deformation algorithm is configured to use different regions of thereference and/or deformable images in different phases of thedeformation algorithm. In some embodiments, this deformation algorithmis configured to use a time dependent weighting factor to control theweight given to two different regions within an image. This timedependent weighting factor may be used in one or more than one phases ofthe deformation algorithm.

Typically, one phase of the deformation algorithm includes the use of anexternal force calculation model configured to change displacement(transform) vectors between parts of a reference image and parts of adeformable image. A smoothing model is typically applied to thesecalculated displacement vectors. As is further described herein,calculation of an image transformation using the deformation algorithmmay be performed in multiple iterations. In some embodiments, theregions (e.g., parts) of the images on which the external forcecalculation model, internal force calculation model and/or flexibilityvalues calculation model operates changes between iterations. In someembodiments, the weighting between regions considered by the deformationalgorithm changes between iterations. In some embodiments, both theidentity and weighting of regions changes between iterations. Theflexibility model may include varying degrees of complexity ranging forma set of constant flexibility values to a bidirectionalmulti-dimensional flexibility model dependent on location as well asdirection of internal and external forces exerted on individual pixels.

A deformable image may be referred to as a medical deformable imageherein when the deformable image is used for medical and/or anatomicalpurposes.

The deformation algorithm is performed by a deformation engine thatincludes an external force computation engine, an internal forcecomputation engine, a matching engine, and a region determinationengine. In some embodiments, the internal force computation engineincludes logic configured to compute and apply a smoothing model. Thesmoothing model can be the Gaussian model used in the demons algorithmor another smoothing model, for example, an exponential smoothing model.The external force computation engine is configured to calculate andapply an external “force” between regions, sets of pixels, and/orindividual pixels of the deformable image. This external force issometimes referred to as a matching force and is opposed to the internalforce.

The image registration system optionally further includes a repositoryconfigured for storing data associated with the deformation algorithm(e.g., data used as inputs and/or generated as interim and/or finalresults). For example, in some embodiments, sets of flexibility values,a priori properties of organs and/or patients, the reference anddeformable images, predetermined regions of the images, or the like, arestored in the repository. Alternative embodiments include a computingsystem, external to the deformation engine, configured for storing partor all of the data associated with the deformation algorithm.

In some embodiments, the image registration system optionally furtherincludes an image generation apparatus for producing the deformableimage. The image generation apparatus may include, for example, an X-raysource and corresponding detector, a computed tomography system, amagnetic resonance system, an ultrasound system, and/or the like.

FIG. 1 illustrates an Image Registration System, generally designated100, according to various embodiments of the invention. ImageRegistration System 100 is configured to deform a deformable image intoalignment with a reference image. This deformation is represented by animage transformation determined using the deformation algorithm asapplied to more than one different region within the images. ImageRegistration System 100 is optionally further configured to performcomputations responsive to the deformed deformable image. For example,some embodiments of Image Registration System 100 include logicconfigured to compute a volume and/or location of an anatomicalstructure in the deformable image, such as the volume of gray brainmatter or a tumor. In various embodiments, the logic comprises aprocessor, other computer readable medium and/or other hardware in whichfirmware and/or software is embodied.

Image Registration System 100 typically includes a Computing System 110.Computing System 110 may comprise, for example, a personal computer,workstation, server, computing devices distributed over a network,and/or the like. Computing System 110 comprises logic configured forperforming computations and/or data processing associated with thedeformation algorithm. This logic includes a Deformation Engine 120discussed further elsewhere herein. In some embodiments, ComputingSystem 110 further comprises a Repository 130 configured to storecomputing instructions, a reference image, a deformable image, and/ordata (e.g., regions, weights, values and/or parameters) associatedtherewith. Repository 130 may include static memory, magnetic memory,random access memory, non-volatile memory, volatile memory, magneticstorage, optical storage, and/or the like. In one embodiment, Repository130 includes dynamic read only memory and a hard disk. For example, insome embodiments, Computing System 110 comprises a hard drive configuredto store data associated with the patient, the reference image, theanatomical structure (e.g., organ) of interest, the deformable image(e.g., a segmented brain atlas), an image transformation, predeterminedregions of interest, initial values associated with the deformationalgorithm, and/or the like.

Deformation Engine 120 is configured for deforming the deformable image,e.g., determining an image transformation, according to a deformationalgorithm. Deformation Engine 120 comprises logic configured forperforming computations and/or data processing associated with thedeforming of the deformable image. This logic may include hardware,firmware and/or computing instructions disposed on a computer readablemedium. In some embodiments, Deformation Engine 120 includes amicroprocessor and associated memory. As is discussed further herein,the deformation algorithm may comprise the demons algorithm, anexponential smoothing algorithm, and/or the like.

In some embodiments, part of Deformation Engine 120 is disposed externalto Computing System 110. Likewise, in some embodiments, part ofRepository 130 is disposed external to Computing System 110.

Image Registration System 100 further optionally includes an ImageGeneration Apparatus 140 configured for generating the reference imageand/or the deformable image. For example, Image Generation Apparatus 140may include a radiation (e.g., X-ray or particle beam) source andassociated detector configured to detect X-rays passed through apatient. In various embodiments, Image Generation Apparatus 140 includesa magnetic resonance imaging device, a computed tomography device, anultrasound device, an X-ray device, and/or other device known in the artto generate internal images of a patient. Image Generation Apparatus 140may be configured to generate two-dimensional, three-dimensional, orfour-dimensional images. In some embodiments, Image Generation Apparatus140 is configured to generate a series of images, for example, athree-dimensional time series of breathing motion. This series is anexample of a four-dimensional image.

Image Generation Apparatus 140 is optionally configured to communicateimage data to Computing System 110 through a direct connection (e.g., acable), over a computing network, or through an alternativecommunication network.

In various embodiments, Image Generation Apparatus 140 is coupled toDeformation Engine 120. In some embodiments, Image Generation Apparatus140 is coupled to Computing System 110. In yet other embodiments, partor all of Image Generation Apparatus 140 is included in Computing System110.

FIG. 2 illustrates various embodiments of Deformation Engine 120.Deformation Engine 120 comprises an External Force Computation Engine210, an optional Internal Force Computation Engine 220, a RegionDetermination Engine 230, and a Matching Engine 240.

Each of these elements may be embodied in hardware, firmware orcomputing instructions within Computing System 110.

External Force Computation Engine 210 is configured for computing anexternal transform to the image transformation. This computation isresponsive to the deformable and reference images. External ForceComputation Engine 210 comprises logic configured for performingcomputations and/or data processing associated with the computation ofthe external transform. For example, in some embodiments, External ForceComputation Engine 210 includes computing instructions embodied on acomputer readable medium and configured to be executed using a processorof Computing System 110.

The external transform computed using the External Force ComputationEngine 210 is configured to apply an external “force” between regions,sets of pixels, and/or individual pixels of the deformable image. Thisforce is sometimes referred to as a matching force, and tends to causespatial compression and/or stretching between neighboring pixels, e.g.,to increase or decrease the magnitude of transform vectors within theimage transformation. This force is opposed to the internal forcevectors. In various embodiments, the external transform comprises a setof scalars, for example a multiplier, representative of the externalforce. In various embodiments, the external transform comprises a set ofmulti-dimensional vectors, each representing an external force on apixel, set of pixels, or region to be applied to the deformable image.In various embodiments, the external transform is based, in part, on thedemons algorithm by Thirion and/or an extension to the demons algorithm,such as but not limited to the extensions described in U.S. applicationSer. No. 11/542,958 entitled “Use of Quality of Match to Estimate ConeBeam CT Reconstruction Artifact Correction Weight in ImageRegistration.” Further details of the demons algorithm can be found in“Image matching as a diffusion process: an analogy with Maxwell'sdemons,” by J.-P. Thirion, Medical Image Analysis, vol. 2, no. 3, pp.243-260, 1998. Alternatively, the external transform may be based onother deformation algorithms.

In various embodiments, the External Force Computation Engine 210 isconfigured to operate on one or more regions of the deformable andreference images. The identity and/or weighting of these regions aredetermined by the Region Determination Engine 230 as is furtherdescribed elsewhere herein. In some embodiments, the identity and/orweighting of the regions changes with time. For example, at the start ofthe image deformation the region considered by the External ForceComputation Engine 210 may include all of each of the deformable imageand reference image. As the image deformation progresses throughsuccessive iterations, the region considered by the External ForceComputation Engine 210 may be a smaller region of the images. Typically,the region considered by the External Force Computation Engine 210 asthe successive iterations near a final result of the deformation processincludes primarily a region of greatest interest, e.g., a regionconfigured to include an area around a treatment volume.

In other embodiments, External Force Computation Engine 210 isconfigured to operate on two regions whose relative contribution to thecalculations of the External Force Computation Engine 210 is determinedby a weighting factor. This weighting factor may change with time. Forexample, a first region may include most or all of the images and asecond region may include an area of greatest interest. At the beginningof an image deformation process, the first region is more highlyweighted, while near the end of the image deformation process, thesecond region is more highly weighted. Further details of theseembodiments are discussed elsewhere herein.

Internal Force Computation Engine 220 is configured for computing theinternal force that tends to reduce excessive spatial compression and/orstretching between neighboring pixels of the deformable image. Suchexcessive compression and/or stretching might otherwise cause a regionof the deformable image to dissolve. Internal Force Computation Engine220 is configured for computing one or more displacement vectors, one ormore accumulators, and/or a smoothing function. Internal ForceComputation Engine 220 comprises logic configured for computing one ormore internal force vectors, one or more accumulators, and/or asmoothing function. For example, in one embodiment, Internal ForceComputation Engine 220 includes a processor and associated computinginstructions configured to determine a displacement vector associatedwith each pixel of a deformable image. The displacement vector is all orpart of a transform vector representative of a displacement of a pixelrelative to an original position of the pixel. For example, thedisplacement vector may be used as a starting value for a transformvector or may be applied to a preexisting transform vector in order tochange the preexisting transform vector. The displacement of one or morepixels results in deformation of the deformable image.

Internal Force Computation Engine 220 is further configured to determinedisplacement vectors responsive to flexibility models. This flexibilitymodel is used to determine the flexibility of the displacement ofpixels, sets of pixels, and/or regions of an image. For example, a pixelassociated with a larger flexibility value may be associated with adisplacement vector of greater magnitude relative to a pixel associatedwith a smaller flexibility value. In various embodiments, theflexibility model is based, in part, on the demons algorithm by Thirionet al., and/or an extension thereto. The flexibility model is describedin further detail elsewhere herein, e.g., in connection with FIG. 5.

Internal Force Computation Engine 220 is further configured forsmoothing (e.g., filtering) the deformable image by applying a smoothingmodel that uses the computed displacement or transform vectors. Invarious embodiments, Internal Force Computation Engine 220 is configuredto use different smoothing models. For example, in some embodiments,Internal Force Computation Engine 220 is configured to use a smoothingmodel based on a Gaussian function. This Gaussian function is optionallysimilar to that used in the demons algorithm and/or an extensionthereto. This Gaussian function is optionally variable duringdeformation of an image. For example, the standard deviation of theGaussian function may be constant or may be changed between successiveiterations of the deformation algorithm.

In some embodiments, Internal Force Computation Engine 220 is furtherconfigured to use a smoothing model based on an exponential function.The exponential function is optionally spatially variant, i.e., it canvary as a function of position and/or direction. These embodiments arefurther described in U.S. application Ser. No. 11/542, 958 entitled “Useof Quality of Match to Estimate Cone Beam CT Reconstruction ArtifactCorrection Weight in Image Registration.”

In various embodiments, the Internal Force Computation Engine 220 isconfigured to operate on regions of the deformable and reference images.In these embodiments, the identity and/or weighting of these regions isdetermined by the Region Determination Engine 230 as is furtherdescribed elsewhere herein. In some embodiments, the identity and/orweighting of the regions change with time. For example, at the start ofthe image deformation the region considered by the Internal ForceComputation Engine 220 may include all of each of the deformable imageand reference image. As the image deformation progresses throughsuccessive iterations, the region considered by the Internal ForceComputation Engine 220 may be a smaller region. Typically, the regionconsidered by the Internal Force Computation Engine 220 as thesuccessive iterations near a final result of the deformation processincludes primarily a region of greater interest, e.g., a regionconfigured to include an area around a treatment volume.

In other embodiments, Internal Force Computation Engine 220 isconfigured to operate on two or more regions whose relative contributionto the calculations of the Internal Force Computation Engine 220 isdetermined by a weighting factor. This weighting factor may change withtime. For example, a first region may include most or all of the imagesand a second region may include a small area of greater interest. At thebeginning of an image deformation process, the first region is morehighly weighted, while near the end of the image deformation process,the second region is more highly weighted. Further details of theseembodiments are discussed elsewhere herein.

In some embodiments, both External Force Computation Engine 210 andInternal Force Computation Engine 220 are configured to use two or moreregions to deform an image. In some embodiments, External ForceComputation Engine 210 but not Internal Force Computation Engine 220 isconfigured to use two or more regions to deform an image. In someembodiments, Internal Force Computation Engine 220 but not ExternalForce Computation Engine 210 is configured to use two or more regions todeform an image. Different regions, or sequences thereof, are optionallyused by the External Force Computation Engine 210 and Internal ForceComputation Engine 220.

Region Determination Engine 230 may include an optional FlexibilityValue Computation Engine 233 and/or an optional Weighting FactorComputation Engine 236, and is configured for determining a region orweighting thereof to be operated on by External Force Computation Engine210 and/or Internal Force Computation Engine 220. There are severalapproaches to this determination, which can be found in variousembodiments of the invention. One approach includes the use of a slidingweighting factor to control the relative consideration given to each oftwo regions. This sliding weighting factor is calculated using WeightingFactor Computation Engine 236.

For example, a first transform may be calculated using a first of thetwo regions and a second transform calculated using a second of the tworegions. These two regions may be two of a series of regions between anentire image and a final region of interest. Alternatively, they may bethe entire image and the final region of interest. The two differenttransforms are then weighted according to a weighting factor w,calculated using Weighting Factor Computation Engine 236, that variesbetween zero and one. In one embodiment the weighting factor is usedaccording to the formula: T_(w) (1−w)T_(a)+wT_(b), where T_(w) is theweighted transform to be applied to the deformable image, T_(a) is thefirst transform calculated using the first region, and T_(b) is thesecond transform calculated using the second region. In otherembodiments, the weighting factor w is applied using a non-linearfunction. T_(w), T_(a) and T_(b) are optionally matrices. The weightingfactor may change during the image registration process. For example, wmay change between iterations of the deformation algorithm. Differentvalues of the weighting factor w or different functions for using theweighting factor w may be used by the External Force Computation Engine210 and the Internal Force Computation Engine 220.

In another example, a single transform may be calculated directly usingboth the first and the second of two regions. The relative weightinggiven to each of the two regions in this calculation is dependent on theweighting factor w.

In various embodiments, the weighting factor w is changed from a valueof zero to a value of one in incremental steps as the image deformationalgorithm progresses. For example, in some embodiments, the weightingfactor is incremented by a constant amount, e.g., by 0.01 over 100iterations. In some embodiments, the weighting factor w is changedresponsive to a quality of match between images. If the quality of matchreaches a value determined prior to calculation of the weighting factorw, then the weighting factor is changed in response. In someembodiments, the weighting factor is changed in response to a change inquality of match as seen between iterations of the image deformationalgorithm. For example, if improvements in the quality of match slowsbetween successive iterations, the value of the weighting factor w maybe increased.

For example, in some embodiments, the selection or weighting of regionsis responsive to a quality of match determined between two images, orregions thereof. For example, if the quality of match reaches athreshold value, the next iteration of the deformation algorithm may beperformed using a different region or weighting thereof. In yet otherembodiments, the selection of regions is responsive to magnitudes ofinternal and external forces. For example, although internal andexternal forces are at equilibrium, both forces may be high or bothforces may be low. When the magnitudes of forces are consistently foundto be high in a specific region, a different region may be used in lateriterations. In still other embodiments, a region is determined based onthe detail available about, for example, an organ and/or a treatmentvolume.

Flexibility Value Computation Engine 233 is configured for computing theset of flexibility values associated with the flexibility model. Thecomputed set of flexibility values is optionally computed using one of aplurality of alternative regions, e.g., either a first region, a secondregion, an intermediate region, and/or a weighted combination of thefirst region and second region. The region used by the Flexibility ValueComputation Engine 233 may change between iterations of the deformationalgorithm. Different regions may be associated with differentflexibility values. For example, flexibility values for a regionincluding the lungs may be greater than flexibility values for a spinalcord.

The set of flexibility values can be determined in various ways. In someembodiments, the set of flexibility values is experimentally determined.For example, the set of flexibility values is determined for each personand/or for each different organ by simulation. In another embodiment,the set of flexibility values is determined by matching a largepopulation of images from multiple individuals to identify typicalvariations between and locations of organs. In some embodiments,boundaries between organs or other areas of interest in a body areconfigured to include specific areas of interest, such as specificorgans or biological structures. These areas of interest may beassociated with different flexibility values.

Other approaches to determining weighting between regions will beapparent to those of ordinary skill in the art, and may be included inalternative embodiments.

In various embodiments, a region to be operated on by External ForceComputation Engine 210 and/or Internal Force Computation Engine 220 isdetermined by calculating an intermediate region based on a first regionand a second region. The intermediate region may be based on aninterpolation between the edges of a first region and the edges of asecond sub region. For example, in one iteration of the deformationalgorithm, the intermediate region may be bounded by a line (or surface)that is 26% of the distance from the first region to the second region.In a later iteration, the intermediate region may be bounded by a line(or surface) that is 28% of the distance from the first region to thesecond region. The percentage distance may change in manners similar tothose discussed elsewhere herein with regard to the weighting factor w.Various intermediate regions may be calculated before or during theexecution of iterations of the deformation algorithm. Thesepre-calculated intermediate regions may be stored in Repository 130.

In some embodiments, the transition between use of the first region andthe use of the second region is more abrupt than in those examplesdiscussed above. For example, the first region may be used by bothphases of the deformation algorithm during initial iterations. After atriggering event, the first region may be used in one phase of thedeformation algorithm and the second region may be used in the otherphase. This approach is equivalent to changing the weighting factor wabruptly from one to zero. The triggering event may be a number ofcompleted iterations, a quality of match achieved, a force magnitude, auser input, and/or the like.

The various examples of using a plurality of regions discussed hereinmay be used in combination. For example, in some embodiments theweighting factor w is kept at one for the first 50 iterations of thedeformation algorithm and then change gradually from 0.5 to zero overthe next 50 iterations.

Matching Engine 240 is configured for matching similar features in thereference image and the deformable image. Matching Engine 240 compriseslogic configured for performing computations and/or data processingassociated with the matching of similar features. For example, in someembodiments, Matching Engine 240 includes computing instructionsembodied in a computer readable medium and configured to be executedusing a processor of Computing System 110. In various embodiments, thematching comprises applying the external force and/or the internal forceto the image transformation In some embodiments, Matching Engine 240 isconfigured for applying the external force prior to the internal force.In other embodiments, Matching Engine 240 is configured for applying theinternal force prior to the external force.

The external force calculated using the External Force ComputationEngine 210 and the internal force calculated using the Internal ForceComputation Engine 220 have opposing effects on the deformation of animage. For example, while the external force tends to promote themovement of pixels so as to minimize the differences between thereference image and the deformable image, the internal force tends tolimit the magnitude of displacement Vectors to avoid a disproportionatedeformation that might otherwise cause the deformable image to dissolve.In some embodiments, Deformation Engine 120 is configured to deform animage (e.g., determine an image transformation) until an equilibrium isfound between the internal force and the external force, on the basis ofindividual pixels, sets of pixels, region of an image, aspecific-subregion, and/or all of an image. At equilibrium, the internalforce and the external force are equal and the calculation of the imagetransform is complete. In some embodiments, Deformation Engine 120 isconfigured to deform an image through iterative application of theinternal and external forces such that an equilibrium is approached.This process may conclude when a specific number of iterations hasoccurred, when the equilibrium is reached, and/or when furtheriterations result in inconsequential deformation. Equilibrium isdescribed in further detail elsewhere herein, e.g., in connection withFIG. 5.

FIG. 3 illustrates various embodiments of Internal Force ComputationEngine 220. Internal Force Computation Engine 220 optionally comprisesIn -Place Flexibility Computation Engine 310 and/or AccumulatorComputation Engine 320. In-Place Flexibility Computation Engine 310 isconfigured to apply a flexibility model, e.g., internal force, withoutfirst calculating internal force vectors. For example, In-PlaceFlexibility Computation Engine 310 may be used as an alternative toInternal Force Computation Engine 220. In-Place Flexibility ComputationEngine 310 makes changes to the image transformation directly, ratherthan by first calculating internal force vectors and then applying thesevectors to the image transformation. The direct modification of theimage transformation may be calculated in ways similar to thosedescribed herein with regard to calculating the internal force vectors.

Accumulator Computation Engine 320 is configured for computing the setof accumulators to be stored in Accumulators Memory 450, discussedelsewhere herein. The set of accumulators optionally allows for thedeformation of the deformable image to decay in a smooth manner, forexample, to avoid large deformations. The set of accumulators may becalculated using a specific region or a weighting between regions. Thisregion or weighting thereof may change between iterations of thedeformation algorithm and may be different from the region or weightingthereof used in calculating flexibility values. Different smoothingmodels may be applied to different regions. For example, an exponentialsmoothing model, in which the set of accumulators can be used to changethe smoothing as a function of position and/or direction, may be appliedto one region, while a Gaussian smoothing model is applied to a secondregion. In the exponential smoothing model, a set of accumulators allowsfor the exponential smoothing model to be performed incrementallypixel-by-pixel in one direction, causing exponential decay. Theexponential decay is controlled by one or more exponential decayparameters within an exponential function of the exponential smoothingmodel. These parameters may be spatially and/or region dependent and areconfigured to determine the degree to which surrounding pixels areincluded in the smoothing as a function of their distance. Unlike aGaussian smoothing model, the exponential smoothing model can includeabrupt changes in the exponential decay as a function of position and/ordirection. This facilitates the deformation of images near abruptchanges in an anatomical structure, for example, at the boundariesbetween organs. In some embodiments, smoothing is applied to all of animage while other steps in the deformation algorithm are applied to asmaller region of the image.

FIG. 4 illustrates various embodiments of Repository 130. Repository 130includes data storage configured for storing data related to ImageRegistration System 100. Repository 130 comprises a Reference ImageMemory 410, Deformable Image Memory 420, Regions Memory 430,Displacement Vectors Memory 440, Accumulators Memory 450, A PrioriProperties Memory 460, Weight Factor Memory 465, Flexibility ValuesMemory 470, Internal Force Memory 475, and/or External Force Memory 480.These memories are configured for storing a reference image, adeformable image, region information, displacement vectors,accumulators, and a priori properties, respectively. In variousembodiments, two or more of these memories share the same computerreadable medium. For example, in various embodiments, Reference ImageMemory 410 and Deformable Image Memory 420 may be combined orimplemented on the same device.

Reference Image Memory 410 comprises a computer readable mediumconfigured to store at least one reference image of a patient. Forexample, Reference Image Memory 410 may be configured to store a set ofimages of a brain of a single patient over the course of treatment.Deformable Image Memory 420 comprises a computer readable mediumconfigured to store a deformable image, for example, a computerizedbrain atlas of a healthy individual, or a previously recorded image of apatient. A reference image stored in Reference Image Memory 410 isoptionally generated using Image Generation Apparatus 140.

In some embodiments, the deformable image is configured to be deformedon a pixel-by-pixel basis to match the reference image such thatstructures of interest can be identified in the reference image. Forexample, within the deformable image, one or more pixels may bepre-associated with specific structures of interest. In the imageregistration process, these pre-associated pixels are matched to pixelsin the reference image. It can then be assumed that these matched pixelsin the reference image are associated with the same structures ofinterest as the pre-associated pixels. Structures of interest within thereference image are thereby identified. This identification can beautomatic when performed under computer control, e.g., performedautonomously by a computing device without human intervention. In someembodiments, the identification of matching structures of interest isused to monitor a change in size, position, or other characteristic of astructure of interest in the same patient over time. In someembodiments, the pre-associated pixels comprise one of the regions ofthe images.

The deformable image stored in Deformable Image Memory 420 is optionallygenerated using Image Generation Apparatus 140. The deformable image mayinclude an image of a patient, an image of a healthy person, or asynthetic anatomical image. In some embodiments, Reference Image Memory410 and Deformable Image Memory 420 share the same device withinComputing System 110. For example, Reference Image Memory 410 andDeformable Image Memory 420 may be included on the same hard drive.

Regions Memory 430 comprises a computer readable medium configured forstoring data associated with regions of the images and/or weightingfactors relating to these regions. For example, Regions memory 430 maybe configured to store pre-computed intermediate regions or weightingfactors for use in determining a relative weight between a plurality ofregions.

Displacement Vectors Memory 440 comprises a computer readable mediumconfigured for storing the displacement vectors computed by InternalForce Computation Engine 220. Displacement Vectors Memory 440 istypically configured to store one displacement vector for each pixel inthe deformable image.

Optional Accumulators Memory 450 comprises a computer readable mediumconfigured for storing sets of accumulators computed by Internal ForceComputation Engine 220. An accumulator is a pixel-by-pixel accumulationfactor associated with the smoothing model. The set of accumulators isused to modify the rate of decay of the smoothing model. For example, inembodiments comprising the exponential smoothing model, AccumulatorsMemory 450 is configured to store the set of accumulators for each pixelaffecting the rate of decay. The role of accumulators is furtherdescribed elsewhere herein, e.g., in connection with FIGS. 4 and 5.

The regions of an image discussed herein may include all of the image ora set of pixels representative of a volume or area within the image. Insome embodiments, a region is specified by a physician or other operatorusing a graphical user interface. For example, a physician may specify aregion by drawing an outline of a cortical surface on the image. In someembodiments, the drawing is made by hand, e.g., by a physician. In otherembodiments, part or all of the drawing is made in an automatic fashion,for example, by an apparatus comprising computer code configured tooutline the region. In some embodiments, a region is specified using theboundaries of a treatment volume. For example, the region may be equalto the treatment volume or a cross-section thereof. The region may beequal to the treatment volume plus a 5 or 10 millimeter boundary aroundthe treatment volume. A region may include disjoint (e.g., unconnected)sets of pixels. For example, a region may include a group of bonesseparated from each other by intervening tissues. Each bone may berepresented by a disjoint set of pixels.

A region may be a subset of another region. For example, a first regionmay include all or a substantial part of an image of a chest cavitywhile a second region may include part of a heart within the chestcavity. A region may include some pixels that are part of another regionand some pixels that are not part of the other region. A region mayentirely surround an area or volume that is not part of that region. Forexample, a first region may include a heart and a second region mayinclude the heart excluding volumes occupied by heart valves.

In some embodiments, regions do not include discrete boundaries. Forexample, a region may include a boundary represented by a Gaussian orexponential distribution. Pixels within these distributions are weightedaccording to the respective distribution function. In some embodiments,regions are defined or bounded by a wavefunction.

A Priori Properties Memory 460 comprises a computer readable mediumconfigured for storing a priori data for use in deformation of an image.A priori data can include data about deformation properties byanatomical structure (e.g., by organ) and/or by patient. For example, apriori data about the lung may include an expected volume of the lung,possible regions within the lung, areas or volumes of interest, and/orthe like.

Optional Weight Factor Memory 465 is configured for storing weightingfactors w calculated using Weighting Factor Computation Engine 236. Thestored weighting factors w may include an array of weighting factors wassociated with one or more specific regions or pixels of an image. Forexample, members of the stored weighting factors w may be mapped todifferent parts of a medical deformable image. Likewise, optionalFlexibility Values Memory 470 is configured to store Flexibility Valuescalculated using Flexibility Value Computation Engine 233. OptionalInternal Force Memory 475 is configured to store internal force vectors,and optional External Force Memory 480 is configured to store externalforce vectors, as may be calculated using Internal Force ComputationEngine 220 and External Force Computation Engine 210, respectively.These various memories may be used to store values between iterations ofa deformation algorithm.

FIG. 5 illustrates methods of deforming an image, according to variousembodiments. Typically, these methods include the use of a plurality ofdifferent regions of an image. For example, in some embodimentsintermediate regions are used in one or more steps of a deformationalgorithm. In some embodiments, a weighting factor is used to weight thecontribution of two different regions in a first phase of a deformationalgorithm while a weighting factor is not used in a second phase of thedeformation algorithm. While the examples illustrated by FIG. 5 includethe use of a weighting factor, it will be apparent to one of ordinaryskill in the art that similar examples may be applied to embodimentsthat make use of intermediate regions. Further, while the examplesprovided include the use of regions in application of the external forceof a deformation algorithm, the use of regions may be applied to otherparts of the deformation algorithm, e.g. all or part of the applicationof the internal force.

In an optional Set Initial Regions Step 510, initial regions aredetermined, for example, using a priori data on deformation propertiesstored in A Priori Properties Memory 460. The initial regions may beselected depending on anatomical structures (e.g., organs) of interestand/or on characteristics of an individual (e.g., patient). For example,a first region may be selected to include a set of anatomical structures(e.g., the lungs, heart, major arteries and veins, and surrounding chestcavity) and a second region may be selected to include one member of theset of anatomical structures (e.g., a major artery). In someembodiments, the first region includes all or a majority of an image andthe second region includes primarily an area of greatest interest. Thisarea of greatest interest sometimes includes a treatment volume.Typically, the first region is selected for greater consideration (i.e.weighting) in early iterations of the image deformation process duringwhich the images are coarsely aligned. The second region is thenselected for greater consideration in final stages of the imagedeformation process in which more precise alignment is achieved. Forexample, the first region may include the pelvic bones, which can beused to roughly align the deformable and reference images, and thesecond region may include the bladder within which a treatment volume islocated.

In some embodiments, identities of the first and second regions areinput to Image Registration System 100 via an interface of ComputingSystem 1 10. For example, a user may designate the first and secondregions by delineating the boundaries of these regions on an imagedisplayed to a user. In some embodiments, the first region includes allof an image and the second region includes a treatment volume bydefault.

In an optional Compute Weighting Factor Step 520, a weighting factor wis determined using Region Determination Engine 230. As is discussedelsewhere herein, this determination may be made using a linear ornon-linear function, and may be responsive to a number of iterations ofthe deformation algorithm performed, a quality of match, force values,and/or the like. As illustrated in FIG. 5, Compute Weighting Factor Step520 may be repeated in different iterations of the deformationalgorithm.

In an optional Compute Flexibility Values Step 525, flexibility valuesare calculated using the first and/or second regions. These flexibilityvalues are calculated using Flexibility Value Computation Engine 233 andare stored in Flexibility Values Memory 470. In various embodiments, thecalculated flexibility values can be defined on a per pixel basis, for aspecific region, or globally. Further details of the calculatedflexibility values are described elsewhere herein. For example, theaccumulator values discussed elsewhere herein may be one example ofthese flexibility values.

In a Compute External Forces Step 530, external forces are calculatedfor both the first and second regions using the External ForceComputation Engine 210. The calculations performed for each of theregions may be performed in parallel or serially. In some embodiments,Compute External Forces Step 530 results in two sets of forcesassociated with the first and second regions respectively. For example,a first set of forces may be associated with the pixels included in thefirst region and a second set of forces may be associated with thepixels included in the second region. Pixels included in both the firstand second regions may be associated with more than one force value.

In a Combine External Forces Step 540, the two sets of forces calculatedin Compute External forces Step 530 are combined using the ExternalForce Computation Engine 210 and the weighting factor w computed inCompute Weighting Factor Step 520. For example, for each pixelassociated with more than one force value, F₁ and F₂, a combined forcevalue F_(c) may be calculated using the equation F_(c)=wF₁+(1−w)F₂.Those pixels associated with one force value are assigned that forcevalue.

In alternative embodiments, Compute External Forces Step 530 and CombineExternal Forces Step 540 are combined into a single step. In theseembodiments, a single set of external forces is calculated usingExternal Force Computation Engine 210, pixels within the two images andthe weighting factor w. By including the weighting factor w in thecalculation of external forces from image pixels, a later combination offorces is not required.

In a Compute Internal Forces Step 550, Internal Force Computation Engine220 is used to compute a set of internal forces. Typically, these forcesare computed on a pixel by pixel basis. This computation may includecomputation of a flexibility model, application of the computedflexibility model to the deformable image to generate a set ofdisplacement vectors, computation of a smoothing function, andapplication of the smoothing function to the set of displacementfactors. In some embodiments, these computations are based on anintermediate region or a weighting between the first and second regions.In various embodiments, one or more of these computations is based ononly one of the first and second regions. For example, in someembodiments Compute Internal Forces Step 550 is performed using only thefirst region. As such, Compute Internal Forces Step 550 is performed onall or a substantial part of the deformable image. In variousembodiments, the substantial part of the deformable image includes atleast 50, 60, 70, 80 or 90% of the deformable image.

The internal force is characterized by a strength as well as a direction(e.g., push for expansion or pull for contraction). Collectively, theinternal force vectors represent the internal force field between thedeformable image and the reference image. A displacement value is thestrength, e.g., magnitude, of a displacement vector. Typically, thedisplacement value is represented mathematically as f(i), 0>i<s, where iis an integer and s equals the size of the grid of pixels.

The computation of the flexibility model in Compute Internal Forces Step550 further comprises computing the set of flexibility values. Thecomputation of flexibility values is performed by Flexibility ValueComputation Engine 233 and may be based on one or more region. The setof flexibility values represents the flexibility of a binding betweenneighboring pixels of the flexibility model. The set of flexibilityvalues is optionally spatially variant, i.e., may depend on positionand/or direction. In some embodiments, different flexibility values areused in different regions. For example, a first set of flexibilityvalues may be used for pixels included in the first region

The flexibility value is optionally represented mathematically as u. Acomplexity of the flexibility model can be increased starting with itssimplest form, in which u is a constant value. Next, u can be madespatially variant with respect to the position of the pixel value i.Thus, u=u(i), in one dimension.

The flexibility value u can be extended to become multi-dimensional.There are several ways to extend u to multiple dimensions. In oneextension, the coordinates are assumed to be separate. For example, fora three-dimensional model, the coordinates can be separated into an x, yand z-axis. A two-way convolution is applied along each of thecoordinate axes separately. This convolution is not circularlysymmetric. However, in practice, this asymmetry does not substantiallyaffect results. In a further approach, the flexibility model is extendedto multiple dimensions by performing a convolution operation alongmultiple discrete axes and thereafter summing the results. Themulti-dimensional model can be made bi-directional in one or moredimensions. The flexibility model can further be made spatially variantwith respect to the direction of stretching.

Compute Internal Forces Step 550 optionally further includes computationof a set of accumulators using Accumulator Computation Engine 420. Thecalculated set of accumulators is typically stored in AccumulatorsMemory 450. In some embodiments, the accumulator is representedmathematically as a_(i), where a_(i)=a_(i−1) (1−exp(−u))+f(i). Thederivative becomes f′(i)=a_(i) exp(−u). The initial value is typicallyset to zero, i.e., a₀=0. The foregoing mathematical formulas can beshown to be equivalent to a discrete convolution of f(i) with exp(−ui),where exp(−ui) is an exponential kernel. An advantage of the exponentialkernel representation is that only the previous accumulator a_(i−1)needs to be stored during the convolution operation. In someembodiments, the mathematical representation of the set of accumulatorsincludes the k×k matrix M.

The smoothing model used in Compute Internal Force Step 550 can comprisethe Gaussian model typically used in the demons algorithm, theexponential kernel model as described in connection with ComputeAccumulators for Internal Force Step 540, or the like. In someembodiments that include the Gaussian smoothing model, the same Gaussianfunction is used in each iteration of the method (e.g., the value of thestandard deviation remains constant between iterations of computationsof the Gaussian smoothing function). In some embodiments, a differentGaussian smoothing model is used in different iterations. For example,the value of the standard deviation may vary between iterations.

In some embodiments, the deformation algorithm comprises a constantscalar smoothing model. In these embodiments, Compute Accumulators forInternal Force Step 540 is optional.

In an Apply Forces Step 560, similar features (e.g., anatomicalstructures) in the reference image and the deformable image are matchedby applying the external force and the internal force to the imagetransformation, subject to the external constraints computed usingExternal Force Computation Engine 210. The computations are typicallyperformed by software and/or firmware. In some embodiments, the softwareand/or firmware is stored in Deformation Engine 120. In otherembodiments, part or all of the software and/or firmware is stored inComputing System 110.

In some embodiments, an Apply In-place Flexibility Model Step 565 may beperformed instead of or in addition to Compute Internal Forces Step 550.In contrast to Compute Internal Forces Step 550, which is used tocalculate an array of internal force vectors that are then applied tothe image transformation, in Apply Forces Step 560, Apply In-placeFlexibility Model Step 565, applies a force by directly modifying theimage transformation. The force can be, for example, a smoothing force.

In a Repeat Step 570, a determination is made as to whether to repeatthe method and, if so, the process returns to Compute Weighting FactorStep 520. Each repetition represents an iteration of the deformationalgorithm. In some embodiments, the method is repeated until equilibriumis reached between the internal and external force. At equilibrium,subsequent iterations do not significantly improve the image match. Forexample, in some embodiments, a determination can be made not to repeatthe method when Apply forces Sep 560 results in transformation vectorchanges with lengths below determined values. In other embodiments, adetermination is made not to repeat the method after a predeterminednumber of iterations has been completed.

If, in Repeat Step 570, a determination is made not to repeat the methodand, therefore, not repeat the process starting with Compute WeightingFactor Step 520, the user of Image Registration System 100 (e.g., atreating physician) can quantify the anatomical structure (e.g., computea volume of gray matter and/or white matter of the brain). Suchquantization has various pathological uses, including determining futuretreatment options. The reference image taken for planning purposes atthe outset of the treatment may then be compared with the deformableimage at equilibrium to identify movement and/or changes in the patientover time. Following the deformation process illustrated by FIG. 5, thedeformed deformable image may be used to accurately identify thelocation of a treatment volume or sensitive organs in order to avoidinjuring the patient with, for example, radiation beams in the spinalcord.

Several embodiments are specifically illustrated and/or describedherein. However, it will be appreciated that modifications andvariations are covered by the above teachings and within the scope ofthe appended claims without departing from the spirit and intended scopethereof. For example, the systems and methods described herein areequally applicable to image registration in applications other than themedical field. Thus, as used herein, terms such as anatomical structuremay be read to include structures other than human biological organs. Invarious embodiments, some or all of the logic disclosed herein is storedin the form of computing instructions on a computer readable medium. Insome embodiments, Deformation Engine 120 and/or Repository 130 arecomprised within Computing System 110. In some embodiments, part or allof one or more steps illustrated by FIG. 5 may be computed by a logiccomponent of Deformation Engine 120 other than the logic componentillustrated in FIGS. 2, 3, and 4. For example, in some embodiments, theinternal and external forces may be computed using the same logic. Logiccomponents described herein may include hardware, firmware or softwareembodied on a computer readable medium. While the examples discussedherein are presented in the context of a pixel-based image system, itwill be apparent to one of ordinary skill in the art that someembodiments are readily adapted to model-based image systems such asthose described in www.sci.utah.edu/research/warping.html. The systemsand methods discussed herein may be applied to two or three-dimensionalimages.

The embodiments discussed herein are illustrative of the presentinvention. As these embodiments of the present invention are describedwith reference to illustrations, various modifications or adaptations ofthe methods and/or specific structures described may become apparent tothose skilled in the art. All such modifications, adaptations, orvariations that rely upon the teachings of the present invention, andthrough which these teachings have advanced the art, are considered tobe within the spirit and scope of the present invention. Hence, thesedescriptions and drawings should not be considered in a limiting sense,as it is understood that the present invention is in no way limited toonly the embodiments illustrated.

1. An image registration system comprising: a deformation enginecomprising: first logic configured to compute a flexibility model usinga first region within a medical deformable image and to apply thecomputed flexibility model to the medical deformable image to generate aset of displacement vectors, the set of displacement vectors beingconfigured to apply a first force to the medical deformable image;second logic configured to compute a smoothing function using the firstregion within the medical deformable image and to apply the smoothingfunction to the set of displacement vectors to create a smoothed set ofdisplacement vectors; third logic configured to compute a transformusing a second region of the medical deformable image, the transformbeing configured to apply a second force to the medical deformableimage, the second force opposing the first force; and fourth logicconfigured to apply the smoothed set of displacement vectors and thetransform to the medical deformable image in one of a plurality ofiterations to deform the medical deformable image to match a referenceimage.
 2. The image registration system of claim 1, wherein the secondregion of the medical deformable image is changed between differentiterations of the iterative process.
 3. The image registration system ofclaim 1, wherein the first region includes greater than fifty percent ofthe medical deformable image.
 4. The image registration system of claim1, wherein the second region is a subset of the first region.
 5. Theimage registration system of claim 1, wherein a boundary of the secondregion is approximated using a distribution function.
 6. The imageregistration system of claim 1, wherein the third logic is configured touse a weighted combination of the first region and the second region tocompute the transform.
 7. The image registration system of claim 6,wherein the third logic is configured to use different weightedcombinations of the first region and the second region to compute thetransform in different members of the plurality of iterations.
 8. Theimage registration system of claim 1, wherein the third logic isconfigured to use the second region to compute the transform in onemember of the plurality of iterations and configured to use the firstregion to compute the transform in a second member of the plurality ofiterations.
 9. The image registration system of claim 1, furthercomprising an image generation apparatus configured to produce themedical deformable image.
 10. An image registration system comprising: adeformation engine comprising: first logic configured to compute aflexibility model using a medical deformable image, and to apply thecomputed flexibility model to the medical deformable image to generatesets of displacement vectors, the sets of displacement vectors beingconfigured to apply a first force to the medical deformable image;second logic configured to compute smoothing functions using a firstregion of the medical deformable image and to apply the computedsmoothing function to the sets of displacement vectors to create asmoothed set of displacement vectors; third logic configured to computetransforms using a first region and a second region of the medicaldeformable image, the transform being configured to apply a second forceto the medical deformable image, the second force opposing the firstforce; and fourth logic configured to apply the smoothed sets ofdisplacement vectors and the transforms to the medical deformable imagein an iterative process to deform the medical deformable image to matcha reference image, at least one iteration of the iterative processincluding use of the first region to compute one of the smoothingfunctions and use of the second region to compute one of the transforms.11. The image registration system of claim 10, wherein the second regionis a subset of the first region.
 12. The image registration system ofclaim 10, wherein the third logic is configured to use a weightingfactor to calculate contributions from the first region and the secondregion.
 13. The image registration system of claim 12, wherein theweighting factor is changed during the iterative process.
 14. A methodcomprising: computing a set of displacement vectors according to aflexibility model using a first region of a medical deformable image,the set of displacement vectors being configured to apply a first forceto the medical deformable image; applying a smoothing function to theset of displacement vectors to create a smoothed set of displacementvectors; computing a transform using a second region of the medicaldeformable image, the transform being configured to apply a second forceto the medical deformable image, the second force opposing the firstforce and being different from the first region; and applying thetransform and the smoothed set of displacement vectors to the medicaldeformable image in an iteration of a deformation algorithm configuredto match the medical deformable image to a reference image.
 15. Themethod of claim 14, wherein computing the transform includes using boththe first region and the second region, and further comprisingcalculating a weighting factor configured to weight the relativecontributions of the first region and the second region to thetransform.
 16. The method of claim 15, further comprising changing theweighting factor between iterations of computing the transform.
 17. Themethod of claim 14, wherein computing the transform includes determiningan intermediate region using the first region and the second region. 18.The method of claim 14, wherein computing the transform includescalculating a first transform using the first region, computing a secondtransform using the second region, and using a weighting factor tocombine the first transform and the second transform.
 19. The method ofclaim 14, wherein the first region includes at least 50% of the medicaldeformable image.
 20. The method of claim 19, wherein some pixels withinthe medical deformable image are included in both the first region andthe second region.
 21. An image deformation engine comprising: firstlogic configured to compute a plurality of weights associated with afirst region within a medical deformable image, and to use the pluralityof weights to determine a first set of force vectors, the first set offorce vectors being configured to apply a first force to an imagetransformation between the medical deformable image and a referenceimage; second logic configured to compute a flexibility model using asecond region within the medical deformable image and to apply theflexibility model to generate a second set of force vectors, the secondset of force vector being configured to apply a second force to theimage transformation, the second force opposing the first force; andthird logic configured to apply the first set of force vectors and thesecond sets of force vectors to the image transformation in one of aplurality of iterations to match the medical deformable image and thereference image.
 22. The image deformation engine of claim 21, whereinmembers of the plurality of weights are mapped to different parts of themedical deformable image.
 23. The image deformation engine of claim 21,wherein the plurality of weights change between members of the pluralityof iterations.
 24. An image deformation engine comprising: first logicconfigured to compute a plurality of weights associated with a firstregion within a medical deformable image and to use the plurality ofweights to determine a first set of force vectors, the first set offorce vectors being configured to apply a force to an imagetransformation between the medical deformable image and a referenceimage; second logic configured to compute a flexibility model using asecond region within the medical deformable image and to apply theflexibility model to the image transformation; and third logicconfigured to use the first logic and the second logic in one of aplurality of iterations to generate the image transformation.
 25. Theimage deformation engine of claim 24, wherein members of the pluralityof weights are mapped to different parts of the medical deformableimage.
 26. The image deformation engine of claim 24, wherein the secondlogic is configured to compute the flexibility model using an in-placeflexibility computation engine.
 27. The image deformation engine ofclaim 24, wherein the first region or the second region changes betweenmembers of the plurality of iterations.